MonteCarloSampling-class: Class '"MonteCarloSampling"'

In sampSurf: Sampling Surface Simulation for Areal Sampling Methods

Description

This is a virtual base class for apply Monte Carlo subsampling methods within a “downLog” or “standingTree” object. See below for subclasses based on the supported sampling methods.

Objects from the Class

A virtual Class: No objects may be created from it.

Slots

stem:

Object of class "Stem": This can be either a downLog or standingTree subclass object.

segBnds:

Object of class "numeric": A vector of length two giving the lower and upper height/length bounds for volume estimation within the bole. All of the following slot definitions below are relative to the segment of the bole defined by these bounds. These bounds correspond to the limits of integration along the bole.

n.s:

Object of class "numeric": The number of Monte Carlo samples (a scalar).

startSeed:

Object of class "numeric": The scalar seed for the random number generator used in the call to the class constructor. Please see the documentation in initRandomSeed for possible values and their meaning. Suffice it to say that storing this in the object allows for object replication. Note that if startSeed = NA, then the seed is not replicable, but the sampling run is by using the random numbers in the u.s slot.

u.s:

Object of class "numeric": The uniform random numbers used in selecting the sampling points along the bole.

description:

Object of class "character": A description of the object if desired (defaults are given for each class).

userArgs:

Object of class "list": Some proxy functions have extra arguments that are required when called from the constructor methods. This slot stores these arguments and their values from the call. This is necessary, e.g., for re-applying a given Monte Carlo method to the (1-u.s) points in antithetic sampling.

Methods

antitheticSampling

signature(object = "MonteCarloSampling"): Allows for antithetic sampling given a subclass object.

show

signature(object = "MonteCarloSampling"): For printing the subclass object.

summary

signature(object = "MonteCarloSampling"): A printed summary of the subclass object.

Author(s)

Jeffrey H. Gove

References

Gove, J. H. 2013. Monte Carlo sampling methods in sampSurf. Package vignette.

See Also

The following subclasses and related: crudeMonteCarlo, importanceSampling, controlVariate, antitheticSampling.

Examples

showClass("MonteCarloSampling")

sampSurf documentation built on March 5, 2021, 5:06 p.m.